Genomic Profiling of a Large Set of Diverse Pediatric Cancers Identifies Known and Novel Mutations across Tumor Spectra
Bibliographic record
Abstract
Pediatric cancers are generally characterized by low mutational burden and few recurrently mutated genes. Recent studies suggest that genomic alterations may help guide treatment decisions and clinical trial selection. Here, we describe genomic profiles from 1,215 pediatric tumors representing sarcomas, extracranial embryonal tumors, brain tumors, hematologic malignancies, carcinomas, and gonadal tumors. Comparable published datasets identified similar frequencies of clinically relevant alterations, validating this dataset as biologically relevant. We identified novel ALK fusions in a neuroblastoma (BEND5-ALK) and an astrocytoma (PPP1CB-ALK), novel BRAF fusions in an astrocytoma (BCAS1-BRAF) and a ganglioglioma (TMEM106B-BRAF), and a novel PAX3-GLI2 fusion in a rhabdomyosarcoma. Previously characterized ALK, NTRK1, and PAX3 fusions were observed in unexpected malignancies, challenging the "disease-specific" alterations paradigm. Finally, we identified recurrent variants of unknown significance in MLL3 and PRSS1 predicted to have functional impact. Data from these 1,215 tumors are publicly available for discovery and validation. Cancer Res; 77(2); 509-19. ©2017 AACR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".